Introduction to Advanced Analytics Maturity Model

December 12, 2018 Advanced Analytics

Sourcing and managing data in the right way is a key competitive differentiator. Companies everywhere are trying understand the right usage of data analytics, and how it can change the course of businesses. CFOs are going to be the leaders in leveraging these advantages that advanced analytics have to offer.

But understanding the role of data specific to each organization can be a daunting task. Several questions arise related to this field, like how to start the process and do it right from the very beginning.

We want to assist you in understanding where you are at. In this post we are going to introduce the analytics maturity model that we use at Digital Intellect to help our clients decide where they stand at the advance analytics journey and how they can get ready to face modern demands. There are six areas of focus that drive the AA maturity model, these are equally important and work in conjunction with each other, even though you might be stronger in some areas rather than all at this time.

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Let’s take a look at these levels and outputs of the Analytics maturity model in detail:

1. Novice

As the name suggests, this is for those who are at the beginner’s level of the advanced analytics journey. In this level, analytics is usually an uncoordinated and ad-hoc activity. Companies at this level have limited access to in-house data, and they are still functioning in an environment where every department collects its own individual data.

A company in the novice stage has analytics that has been demarcated for each department. These basic analytics support siloed tactical operations and there is no sort of integration of data at any level.

2. Intermediate

This is slightly higher than a beginner’s level where analytics has taken a strategic and operational focus, but not as much as it should. Companies at this level integrate data from various departments for greater insights in order to understand patterns and drive decision making.

At the intermediate level, a company functions with integrated business analytics from multiple data sources. Non-segregated data is visualised together for greater strategic insights to support operational life cycle management and strategy.

3. Advanced

This is the stage you want to be at. Companies in the advanced stage of the model use analytics to drive transactions in real-time seamlessly throughout the business. Such in-depth application of analysis helps deliver improved commercial outcomes and drive operational efficiencies. Departments work together to product competitive business insights that enhance the financial performance of the business.

At this level, the output relies on advanced predictive analytics and machine learning. Real time insights are used to drive operational transactions to deliver improved outcomes and efficiencies through the organization.

advanced analyticsOnce you know where you are, you will have to adopt a mentality of continuous improvement and integrate data into your decision making process. Here are six dimensions you need to master in the process –

1. Operating rhythm

Before using analytics, first examine how analytics can be applied to your business to achieve operational efficiency and financial performance. The integration of analytics to make strategic decision needs to be evaluated well in advance by looking at the relationship between operation and business strategy.

2. Capabilities

Make sure you have the right people in your team to achieve organizational goals. Evaluate the nature of your team composition, skill sets required and avenues for collaboration between technical and operational teams.

3. Information management

Analytical output will only be as valuable as the quality and quantity of the data inputs. Evaluate the various dimensions of data sourcing, management and preparation capabilities.

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4. Performance insights

Analytics relies on a host of statistical and data mining techniques. If data analysis is not connected strongly to business and financial metrics, the analytics exercise might face failure. Before undergoing any analytical exercise, assess the use of the techniques available along with the metrics used to measure successful operational programs. You should also assess the relationship between operational metrics and financial metrics, breadth of statistical methods used, and the strength of the ROI capabilities of pertaining to analytics investment.

5. Execution

Effective analytics requires a well-defined process of generating, disseminating and applying it to derive operational insights. Evaluate the process by which your organization prioritises analytical projects.

6. Tools

The tools that you use will define the strength of your analytical capability. These are mainly of three kinds – predictive tools, business intelligence tools or cross channel campaign management tools. No matter which one you use, ensure that you thoroughly evaluate the depth and breadth of the technology so that it supports your goals.

No matter which stage you are at, be it novice, intermediate or advanced, we can help you up your game with the right analytical strategies. We will be explaining each of these six areas in detail in the coming blogs.

Do let us know your thoughts or suggestions in the comments section. For any queries, you can write to me directly at .